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Home-based work, time endowments,
and subjective well-being: Gender
differences in the United Kingdom

Gimenez-Nadal, Jose Ignacio and Velilla, Jorge

University of Zaragoza, BIFI, CTUR, University of La Rioja

2020

Online at https://mpra.ub.uni-muenchen.de/104937/
MPRA Paper No. 104937, posted 28 Dec 2020 18:38 UTC

Home-based work, time endowments, and subjective well-being:
 Gender differences in the United Kingdom *

 José Ignacio Giménez-Nadal, University of Zaragoza and BIFI (Spain)

 Jorge Velilla, University of La Rioja (Spain)

Abstract
The confinement caused by Covid-19, and the associated promotion of telework to reduce exposure of
workers to the disease, have clear implications for worker daily behaviors and well-being. This paper
empirically explores the differences between commuters’ and teleworkers’ time allocations during their
workdays, and the instant enjoyment experienced while doing such activities, with a focus on gender
differences. Using detailed information from the UK Time Use Survey for the years 2014-2015, the
results show a statistically significant cut in female and male paid work time associated with teleworking.
On the other hand, teleworkers spend more time than commuters in unpaid work and leisure activities.
The results also reveal a cut in women’s experienced enjoyment while doing telework, while male
teleworkers enjoy their leisure more than do commuters. These results suggest that confinement policies
promoting teleworking may impact not only worker time allocations, but also individual well-being, and
such an impact may differ between men and women, leading to intrahousehold imbalances.

Keywords: Gender difference; telework; time use; subjective well-being; UKTUS

* Correspondence to J. Velilla. Department of Economics and Business Studies, University of La Rioja. C/ La

Cigüeña 60, 26006 Logroño, Spain. Email: jorge.velilla@unirioja.es
This paper has benefitted from funding from the Government of Aragón (Project S32-20R), and from the Spanish
Ministry of Science (Project PID2019:108348RA-I00).

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1. Introduction
The onset of the COVID-19 pandemic has led to confinements of the population worldwide,
which has clear implications for worker’s daily behavior. Many have been forced to work from
home (i.e., to telework), changing their behavior in comparison to the pre-pandemic period. A
clear consequence of these changes is that the time spent with spouses, children, and/or other
family members will have increased, including working while other members of the family are
present. These changes may have led to changes in the well-being of workers in their activities,
since activities done in the presence of others are seen as being more beneficial in comparison
with activities done alone (Kahneman et al., 2004; Kahneman and Krueger, 2006; Krueger, 2007:
Sevilla et al., 2012; Gimenez-Nadal and Molina, 2015). It is unclear whether confinements have
increased intrahousehold inequality in well-being, as women and men may have different
preferences for time use, work schedules, togetherness, and other factors that affect individual
well-being. In this context, it is important to study the potential gender differences in terms of
teleworking, the timing of daily activities, and satisfaction, which may be of special interest in
understanding the impact of confinement on female and male workers’ wellbeing.

 Existing research has analyzed the impact of confinements during the Covid-19 pandemic
on workers. For instance, Hamermesh (2020) analyzed who individuals spend their time with
under a simulated lockdown, using US data from years 2012-2013, showing that married
individuals’ time with the spouse increased during a lockdown, resulting in an increase of the
couple’s overall life satisfaction, while the opposite is the case for singles. Gimenez-Nadal et al.
(2020a) ran a similar analysis in the UK, studying the instant utility (i.e., experienced well-being)
of individuals, finding differential results between women and men, and also differences between
the US and the UK. On the other hand, Del Boca et al. (2020) analyzed a dataset collected in
April 2020, in Italy, to study how Covid-19 is associated with changes in couples’ working
arrangements (market work and housework). Their results reveal that women spent more time
doing housework during the Covid-19 lockdown, regardless of the couples’ paid work
arrangements, while husbands’ unpaid work depends on wives’ paid work arrangements.

 Thus, the existing literature on the impact of confinements (as a consequence of the Covid-
19 lockdown) on worker’s time allocations suggests the existence of an asymmetric effect on
women and men, which may be key in determining how confinements have affected household
well-being. In this context, we study how telework, which has been actively promoted during

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confinements, is associated with female and male workers’ time allocations and well-being. Other
authors analyzing the impact of Covid-19 on workers are, for instance, Alon et al. (2020), Biroli
et al. (2020), Gershuny et al. (2020), and Mangiavacchi et al. (2020).

 Telework, or home-based work, has previously been analyzed in different contexts and
disciplines, although the assumed positive impacts of teleworking, in terms of work-family
flexibility, reduced pollution and congestion, and increased worker productivity are not robustly
documented in the existing empirical literature (Edwards and Field-Hendrey, 2002; Safirova,
2002; Rhee, 2008; Bloom et al., 2015; Dockery and Bawa, 2018). For instance, telework has often
been found to reduce work-family conflicts, but some authors have also found negative
outcomes in terms of decreased work inclusion and co-worker satisfaction (Morganson et al.,
2010; Golden and Fromen, 2011). Furthermore, what little applied research analyzing the impact
of telework on individual time allocation decisions there is – and analysis of impacts on worker
well-being - shows only mixed results. Some authors have found that teleworkers work longer
hours than commuters (Peters and van der Lippe, 2007; Golden, 2008), while other authors have
concluded the opposite (Wight and Raley, 2009; Gimenez-Nadal et al., 2020b). Therefore, it is
unclear whether the systematic promotion of telework is beneficial for workers’ well-being, even
in a pandemic situation requiring lockdowns.

 Within this framework, we empirically explore worker’s time allocation decisions during
their workdays, with a focus on the differences between commuters and teleworkers, using
detailed time use diaries from the UK Time Use Survey for the years 2014-2015. The results
show a cut in paid work time associated with telework, in line with prior studies analyzing market
work time during a pandemic situation (Hamermesh, 2020). Specifically, net of observable
characteristics, the average male (female) who works from home devotes about 34.3% (50.6%)
less time to paid work activities, relative to the individual who commutes to/from work. These
differences correspond to cuts in paid work time per week of about 221 minutes for female, and
106.5 minutes for male teleworkers, compared to commuters. Second, telework is associated
with an increase in unpaid work and leisure time. Male (female) teleworkers devote about 63.3%
(46.5%) more time to housework and unpaid work activities, and 20.2% (26.2%) more time to
leisure, relative to male (female) commuters.

 We also analyze whether being a teleworker is associated with different experienced utility
during the day for workers. To that end, we assess the enjoyment experienced during episodes

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of paid work, unpaid work, and leisure, focusing on differences in enjoyment between
commuters and teleworkers. We observe that female teleworkers enjoy their paid work episodes
less than do female commuters, but they also enjoy their unpaid work episodes less. The results
show no differences between male teleworkers and commuters in the enjoyment experienced
while doing paid and unpaid work activities, although male teleworkers enjoy their leisure
episodes more than do commuters.

 The contributions of the paper are, then, threefold. First, we compare the time allocation
decisions of teleworkers and commuters, with a focus on paid work time, unpaid work time, and
leisure time. The results may help planners and policy makers to anticipate the future impacts of
promoting telework as part of work-family and self-employment policies designed during
lockdown situations (Campaña et al., 2020; Molina, 2020a; Molina, 2020b) Second, we focus not
only on paid work activities, or on aggregate life satisfaction indicators (Hamermesh, 2020; Del
Boca et al., 2020; Gimenez-Nadal et al., 2020a), but also analyze other time uses that may be
affected by teleworking. We observe that teleworking is related to the instant enjoyment levels
obtained during non-paid work (unpaid work and leisure) activities. Third, our analysis reveals
gender differences in how telework may impact workers’ instant enjoyment

 The remainder of the paper is structured as follows. Section 2 describes the data and the
variables used in the analysis. Section 3 describes the relationship between telework, on the one
hand, and paid work, unpaid work, and leisure, on the other. Section 4 compares the instant
enjoyment experienced while doing these activities, and the differences in that enjoyment
between commuters and teleworkers. Finally, Section 5 concludes.

2. Data and variables
We use the UK Time Use Survey (UKTUS), for the years 2014-2015. The UKTUS is the official
time use survey of the UK (Gershuny and Sullivan, 2017), and provides socio-economic and
time use information covering respondents’ activities during the 24 hours of the day, from 4 am
to 4 am of the next day. 1 Time use diaries produce more accurate estimates than surveys based

1 Among respondent households, all the household members aged 8 and older are interviewed, being asked to

complete two time use diaries on two different days (a weekday and a weekend).

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on stylized questionnaires (Bonke, 2005; Yee-Kan, 2008) and thus have become the gold
standard in the analysis of individual daily behaviors (see Harms et al., 2019).

 The UKTUS allows us to define several categories of workers’ uses of time. For our analysis,
we focus on episodes of paid work, leisure, and unpaid work. We define paid work activities as
activities including “paid work, main job”, “second or other job”, “travel as part of work”
(excluding commuting time), “work breaks”, and “other time at workplace”. We follow the
definition of teleworking from Gimenez-Nadal et al., (2020b), and define a dummy variable that
takes value 1 for those individuals who report not having commuted to/from work on their
working days, and value 0 otherwise. 2 For leisure and unpaid work time, we follow the definition
of Aguiar and Hurst (2007) and Gimenez-Nadal and Sevilla (2012). Leisure includes activities
such as watching TV, sports, out-of-home leisure, gardening, pet care, socializing, and so on.
Unpaid work time, or household work, is defined as those activities related to household chores
and domestic activities (cooking, setting the table, washing, cleaning, laundry, ironing, clothing,
repair, etc.).

 Additionally, the UKTUS includes information on enjoyment ratings of all episodes in the
diary, intended to compute the instantaneous well-being experienced by individuals in their daily
activities (i.e., hedonic feelings). In this sense, the day after the diary day (following the “day
reconstruction method”, Kahneman et al., 2004; Kahneman and Krueger, 2006), respondents
provide a value, for each activity, to the following question: “How much did you enjoy this
time?”, taking values from 1 (“not at all”) to 7 (“very much”).

 We restrict the sample to workers who filled-in their diaries on working days, defined as
those days when workers spent 60 or more minutes in paid work activities (excluding
commuting). This restriction excludes from the sample those individuals not in paid work, as we
retain only employed individuals who worked on the diary day. To minimize the role of time
allocation decisions over the life cycle, we restrict the sample to individuals between 21 and 65

2We used three alternative definitions of teleworkers, exploiting the information available in the UKTUS regarding
where activities take place. Thus, the first alternative definition defines teleworkers as those individuals who do
some paid work at home. The second identifies teleworkers as those who spend at least 1 hour doing paid work at
home. The third definition identifies teleworkers as those who do all their paid work at home. Table A1 in the
Appendix summarizes all the definitions of teleworkers used in the analysis. Results are qualitatively similar for the
default identification of teleworkers (i.e., zero commuting time), and for the three alternative definitions, suggesting
that the analyzed differences between home-based workers and commuters do not strongly depend on the definition
used in the analysis.

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years old (consistent with Aguiar and Hurst, 2007; Gimenez-Nadal and Sevilla, 2012). Finally,
we omit individuals with missing information for any of the relevant variables, as is standard
practice. The analysis is then performed at the episode level, which leaves a sample of 23,274
episodes of paid work, 12,686 episodes of unpaid work, and 18,981 episodes of leisure. These
episodes correspond to 3,076 individuals, of whom 1,567 are women, and 1,509 are men.

 The UKTUS data allow us to define additional control variables at the individual level,
including: the gender of respondents, age, formal education, native status, marital status,
household composition (the number of family unit members, and the number of children), and
employment status (identifying self-employed workers, and full-time workers). For education,
we define three dummies in terms of the maximum level of formal education completed: primary
education, secondary education, and University education. Finally, the UKTUS allows us to
define dummies identifying the following regions: “North East”, “North West & Merseyside”,
“Yorkshire & Humberside”, “East midlands”, “West midlands”, “East of England”, “London”,
“South East”, “South West”, “Wales”, “Scotland”, and “Northern Ireland”.

2.1 Descriptive evidence

Table 1 shows summary statistics of episode variables, for male and female commuters and
teleworkers, along with p-values for the differences between commuters and teleworkers. All the
statistics are computed using sample weights defined at the episode level, provided by the
UKTUS survey. Focusing on paid work episodes, the average female commuter does 7.5
episodes of paid work per day, with the average episode lasting about 102.2 minutes, and
reporting an average enjoyment of 4.0 out of 7. On the other hand, female teleworkers report
6.3 episodes of paid work per day, with an average duration of 70.7 minutes, and an average
enjoyment during these episodes of 3.7 out of 7. Differences between commuters and
teleworkers in these variables are all statistically significant at standard levels. This indicates that
female commuters do more and longer paid work episodes per day than female teleworkers, and
the enjoyment experienced during these activities is reported to be higher among commuters
than among teleworkers. On the other hand, among males, commuters (teleworkers) do 7.6 (7.9)
episodes of paid work per day, with the difference between them not being significant at standard
levels. However, the average duration of a paid work episode among commuters is 112.6
minutes, vs 80.8 minutes among teleworkers, with this difference being highly significant. The

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average enjoyment associated with paid work activities is greater among male teleworkers than
among male commuters (4.1 vs 3.6, out of 7, respectively), suggesting that male teleworkers work
shorter periods and enjoy these periods more than their commuter counterparts.

 Regarding the episodes of housework, or unpaid work, Table 1 shows that female
commuters do 5.0 episodes of unpaid work per day, lasting on average 18.0 minutes, and with
an associated enjoyment level of 3.9 out of 7. Female teleworkers, on the other hand, do 7.0
episodes of unpaid work per day, with an average duration of 26.0 minutes, and an equal
experienced enjoyment of 3.9. Differences in the number of periods and the average duration
of periods are significant at standard levels, suggesting that female teleworkers do more and
longer episodes of unpaid work, compared to female commuters in the sample. However, the
difference between commuters and teleworkers in terms of the enjoyment associated with
unpaid work episodes is not statistically significant at standard levels. For males, results are quite
similar, as male commuters do 2.6 episodes of unpaid work per day, lasting on average 14.4
minutes, vs 3.9 episodes of 20.3 minutes, on average, for male teleworkers, with differences
being statistically significant. However, male teleworkers seem to enjoy more their unpaid work
activities than do male commuters, with average enjoyment rates of 3.8 among teleworkers and
3.2 among commuters, with the difference being highly significant.

 Focusing on leisure activities, female commuters (teleworkers) have 6.1 (7.3) episodes of
leisure per day, with each period lasting on average 37.1 (39.4) minutes. Furthermore, the average
enjoyment of these episodes is 4.9 and 4.8 for commuters and teleworkers, respectively.
Differences in these magnitudes are significant only for the number of periods of leisure,
suggesting that female teleworkers have more episodes of leisure, but neither the duration nor
the experienced enjoyment of these episodes differ between commuters and teleworkers. For
males, on the other hand, commuters have 5.8 episodes of leisure per day, lasting on average
40.4 minutes, and reporting an average enjoyment of 4.6 out of 7. For male teleworkers, the
average number of leisure episodes per day is 5.6, the average duration of each of these episodes
is 44.5, and the average enjoyment experienced is 2.3 out of 7. Differences between commuters
and teleworkers are significant in the number of leisure episodes, suggesting that male
teleworkers have more leisure episodes than their commuter counterparts, but also in the
enjoyment experienced, which suggests that male teleworkers enjoy their leisure episodes less

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than do male commuters. The difference in the average duration of leisure episodes is not
significant at standard levels.

 Table 2 shows summary statistics of the main variables defined at the individual level, for
male and female commuters and teleworkers in the sample, including p-values for the
differences. Focusing on the daily minutes spent on these activities, the average paid work time
of female commuters (teleworkers) is 436.5 (310.5) minutes per day, while the corresponding
average for males is 489.9 (403.7) minutes per day. The difference between commuters and
teleworkers is statistically significant at standard levels for both women and men (p < 0.001),
suggesting that teleworkers spend less time in paid work activities than do commuters (126 fewer
minutes, and 86.2 fewer minutes, among women and men, respectively). This difference seems
consistent with opposite-direction differences between commuters and teleworkers in unpaid
work time, and in leisure time. Female commuters spend every day, on average, 89.6 minutes in
unpaid work activities, and 182.8 minutes in leisure activities, vs 148.7 and 231.1 minutes spent
in those activities by teleworkers. These differences are statistically significant at standard levels
(p < 0.001). Among males, commuters spend, on average, 45.7 minutes in unpaid work, and
198.8 minutes in leisure, vs 82.1 and 254.7 minutes spent in unpaid work and leisure by
teleworkers. Differences between male commuters and male teleworkers are also statistically
significant (p < 0.001).

3. Teleworker and worker time allocations
The first objective of the analysis is to compare the time allocation decisions of female and male
teleworkers and commuters, and thus explore how these workers distribute their available time
throughout their working days. One could anticipate the impact of confinements and other
measures encouraging telework, in a health crisis such as Covid-19. Differences shown in Tables
1 and 2 represent only raw differences between commuters and teleworkers, and it is possible
that certain worker attributes, such as socio-demographics, regional characteristics, and labor-
related factors, may be driving these results. To partially overcome this issue, in this section we
propose an empirical analysis, resembling that in Gimenez-Nadal et al. (2020b) for the case of
the US, to study the differences in the amount of time spent by male and female teleworkers and
commuters in paid work, unpaid work, and leisure activities, net of observable characteristics.

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For a given individual i, consider that is the time spent by worker i in the reference
activity. We then estimate, by Ordinary Least Squares (OLS), the following equations:

 log(1 + ) = 0 + 1 + 2 + + , (1)

where is a dummy variable taking value 1 if i is a teleworker, 0 otherwise; is a vector of
individual-level controls, represents region fixed effects, and is the error term. The
dependent variable is defined in logarithms in order to interpret the estimated coefficients in
percentage levels (i.e., elasticities). We sum 1 to avoid problems computing logarithms for
individuals reporting no unpaid work time, or no leisure time. 3 All the equations are estimated
separately for female and male workers, and estimates include sample weights provided by the
UKTUS survey, as well as robust standard errors. Table A2 in the Appendix shows similar
estimates using the alternative identification of teleworkers. Results are qualitatively similar.

 Columns (1) and (2) of Table 3 show estimates on paid work time for women and men,
respectively. The results suggest that, net of observed characteristics, female teleworkers spend
about 50.6 percent less time than do similar commuters. Among males, teleworkers spend on
paid work about 34.3 percent less time than commuters, net of observed heterogeneity. These
results are consistent with the descriptive results shown in Tables 1 and 2, suggesting that female
and male teleworkers work fewer hours than their commuter counterparts. Furthermore, these
percentages correspond to 220.9 fewer minutes for females, and 106.5 fewer minutes for males,
with differences between women and men being statistically significant at standard levels,
according to a t-type test (p = 0.028). Thus, it seems that the conditional correlation between
being a teleworker, and paid work time, is greater for women than for men.

 Columns (3) and (4) of Table 3 show estimates on unpaid work time for women and men.
Among female workers, being a teleworker is associated with a statistically significant increase

3 The sample is restricted to individuals devoting more than 60 minutes to paid work, while there may be individuals
in the sample reporting 0 minutes of leisure or unpaid work time. Specifically, 431 individuals in the sample report
0 minutes of unpaid work, while 89 individuals report 0 leisure time. Given that we are actually observing individuals
reporting zero unpaid work and zero leisure, an alternative would have been to estimate censored or truncated
regressions, such as Tobit models (Tobin, 1958). Nevertheless, prior research has shown that OLS and Tobit models
produce similar estimates when studying time allocation decisions (Frazis and Stewart, 2012; Gershuny, 2012; Foster
and Kalenkoski, 2013). Additionally, these models are appropriate when variables are censored, and working on
time use data such censoring implies that individuals may want to spend less-than-zero minutes in activities. By
assuming that no one can spend negative time in leisure and unpaid work, censoring is no longer needed, and then
OLS and Tobit should give equivalent answers. Therefore, we have decided to rely on OLS estimates, as is common
in the literature.

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in unpaid work time of about 46.5 percent, which corresponds to teleworkers doing, on average,
about 41.7 more minutes per day of unpaid work activities than a similar commuter. The average
male teleworker, on the other hand, does 63.3 percent more unpaid work than the similar
commuter, representing 28.9 more minutes, and indicating that being a teleworker is correlated
with more unpaid work time. The relative difference (the difference in percentage) between
commuters and teleworkers is greater for males than for females, although it is not statistically
significant, according to a t-type test (p = 0.308), while the raw difference in minutes is estimated
to be quantitatively larger for women than for men.

 Finally, Columns (5) and (6) of Table 3 show the results of estimating Equation (1) on leisure
time. The estimates reveal a positive and statistically significant correlation between being a
teleworker, and the time spent in leisure activities. Among females, the difference is about 26.2
percent more leisure time for teleworkers, with the estimated coefficient being highly significant.
For men, on the other hand, the coefficient is statistically significant only at the 90% level,
indicating that male teleworkers spend 20.2 percent more time in leisure activities than do similar
commuters. These differences represent a gap between teleworkers and commuters of about
47.9 minutes for females, and 40.2 minutes for males, net of observable characteristics. 4

4. Telework and worker instant enjoyment
We now analyze the instant enjoyment experienced by individuals while doing paid work, unpaid
work, and leisure activities, with a focus on the differences between commuters and teleworkers.
we want to determine whether policies encouraging or suggesting teleworking under different
settings, such as confinements, may influence workers’ well-being. To that end, we estimate OLS
models, for female and male workers, for a given individual i, and episode j, as follows;

 = 0 + 1 + 2 + 3 + + , (2)

4 For the shake of brevity, we only describe the main coefficients of interest. For the same reason, we do not show
the analysis of the timing of paid work, unpaid work, and leisure, analyzed as the rate of teleworkers and commuters
doing the corresponding activities during the 24 hours of the day (Hamermesh, 1999). Table A3 in the Appendix
shows the rate of female and male commuters and teleworkers doing these activities, by the hour of the day (divided
in 24 1-hour time bands). The results show that there are more commuters than teleworkers working during
standard work hours (e.g., 8am to 4pm), and opposite trends in terms of leisure and childcare. Results are mostly
analogous to those in Gimenez-Nadal et al. (2020b) for the US.

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where , , and are defined as in Equation (1), and is the error term. The dependent
variable, , is the enjoyment reported by individual i while doing the activity reported in period
j, and is a vector of episode-level controls. This vector includes dummies for the time band
in which period j began, the duration of period j, and two dummies capturing whether the spouse
was present (value 1, 0 otherwise), or whether a household child was present (value 1; 0
otherwise), during period j. Equation (2) is estimated separately for episodes of paid work, unpaid
work, and leisure time. All the estimates include sample weights at the episode level, provided
by the UKTUS survey. Standard errors are clustered at the individual level to take into account
the heterogeneity of time allocation decisions as well as inter-personal differences in scales
(Ferrer-i-Carbonell and Frijters, 2004). Table A4 in the Appendix shows similar estimates using
the alternative identifications of teleworkers.

 Columns (1) and (2) of Table 4 show estimates of Equation (2) for the episodes of paid work.
Focusing on the main explanatory variable of interest, estimates show that, for female workers,
teleworking is negatively correlated to the enjoyment experienced while doing paid work. The
corresponding coefficient is statistically significant at standard levels, indicating that female
teleworkers experience less enjoyment while doing paid work than do female commuters.
Among males, estimates show a not statistically significant difference for teleworking in the
experienced enjoyment associated with paid work episodes.

 Regarding episode characteristics, the duration of the paid work episode is negatively
correlated with the experienced enjoyment. The presence of the (married or unmarried) couple
is not significant for females, but positive and highly significant for males, suggesting that males
enjoy the paid work episode more when teleworking if the couple is present at that moment.
Conversely, the presence of a child is positive and highly significant for females, but negative
and significant for males, indicating that female workers enjoy their paid work episodes more
when teleworking if their child (children) is (are) present, while the opposite applies to males. 5

 Columns (3) and (4) of Table 4 show the results of estimating Equation (2) on the episodes
of unpaid work. Teleworking is also negatively correlated with the enjoyment experienced while
doing unpaid work for females, with the coefficient being statistically significant at standard
levels, and is lower for female teleworkers, than for female commuters. Among males, estimates

5 Estimates associated with the individual-level controls are available upon request.

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show no differences for teleworking in the experienced enjoyment when doing unpaid work
activities.

 For the episode characteristics, the duration of the unpaid work episodes is not statistically
significant among female workers, indicating that the enjoyment experienced does not depend
on the duration of the unpaid work episodes. However, among males, the duration of unpaid
work episodes shows a negative and highly significant correlation with the experienced
enjoyment while doing these activities. The presence of the couple is positive and statistically
significant for females, but not significant for males, while the presence of a child is positive and
statistically significant for both females (at the 99% level) and males (at the 90% level).

 Columns (5) and (6) of Table 4 show the results of estimating Equation (2) on leisure
episodes. Among females, the estimated coefficient associated with the dummy identifying
teleworking is not statistically significant at standard levels, indicating that the experienced
enjoyment while doing leisure activities is similar for female teleworkers in comparison to
commuters. However, among males, there is a positive and statistically significant correlation
between teleworking, and the enjoyment experienced during leisure, indicating that male workers
enjoy their leisure episodes more if they are able to telework, net of individual and episode
observable characteristics.

 The duration of the leisure episodes is not significant among females, but positive and
statistically significant among males. Thus, it seems that the enjoyment experienced during
leisure does not depend on the duration of the activity for females, while males get more
enjoyment from longer episodes of leisure. The presence of the couple is positive and highly
significant for both males and females, indicating that doing joint leisure is preferable to other
forms of leisure, in line with Cosaert et al. (2020) and Hamermesh (2020). The presence of a
child is positive and highly significant for females, but not significant for males, and female
workers seem to enjoy their leisure activities more in the presence of a child, while males are
indifferent to the presence of the child, in terms of the enjoyment experienced.

5. Conclusions
This paper explores how teleworking relates to workers’ time allocation decisions on workdays,
and the instant enjoyment experienced, with a focus on differences between women and men.

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Using the UK Time Use Survey for the years 2014-2015, the results show that telework is
associated with a cut in paid work time for both, and is associated with increased unpaid work
and leisure times, with these differences being statistically significant, and quantitatively relevant.
Additionally, the cut in paid work time associated with teleworking is greater for women than
for men, revealing a potential gender difference in the impact of lockdowns on households, in
line with Del Boca et al. (2020) and Sevilla and Smith (2020). Furthermore, the results also show
that teleworking relates to a decrease in women’s, but not men’s, experienced enjoyment while
working in the labor market and in the household. On the other hand, teleworking for men is
related to greater enjoyment during leisure activities. These opposite effects for women and men
suggest that promoting teleworking may impact men and women differently, producing
intrahousehold imbalances by increasing enjoyment for one while decreasing enjoyment for the
other.

 The empirical analysis has certain limitations. First, the identification of teleworkers is not
standard in the literature. We follow a similar definition as in Gimenez-Nadal et al. (2020b), and
also run some robustness checks with alternative definitions, but we must acknowledge
measurement error. Second, the data is cross-sectional, and thus all the analysis is limited to
conditional correlations only, as we cannot account for reverse causality and endogeneity. Thus,
the results cannot be interpreted as showing causal links, but only correlations, net of observable
factors. Third, as we do not yet have time use diaries collected during lockdowns, the results
should be extrapolated. Hence, conclusions should be taken with caution.

 Despite these limitations, the results shown in this paper are important for society,
especially in a period of health uncertainty, such as the continuing Covid-19 global crisis, which
may entail months of lockdown, where home-based work becomes more relevant for workers
and employers, and also policy makers, beyond purely speculative claims. The results are relevant
for workers, as being a teleworker has traditionally been associated with decreased work-family
conflicts, as individuals who are able to telework seem to be able to spend more time in unpaid
work activities during the day and, particularly, during regular working hours. However, our
results suggest that women may experience a decrease in their daily enjoyment while teleworking.

 For firms, the results reveal decreased working hours associated with teleworking and
home-based workers, although the literature is not clear about whether this leads to decreased
productivity (Ross and Zenou, 2008; van Ommeren and Gutiérrez-i-Puigarnau, 2011; Bloom et

 13

al., 2015; Gimenez-Nadal et al., 2020b). Further research should investigate whether or not
teleworkers are more productive than commuters. Finally, the results are important for planners
and policy makers, who must regulate telework, and create policies associated with home-based
working in general terms, but also in periods of lockdown and confinement, as has happened in
recent months, since the onset of the Covid-19 pandemic.

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 17

Table 1. Summary statistics of episode variables
 FEMALES MALES
 Commuters Teleworkers Diff Commuters Teleworkers Diff
 VARIABLES Mean S.D. Mean S.D. p-value Mean S.D. Mean S.D. p-value

 Paid work episodes per day 7.520 6.362 6.328 5.610 (0.006) 7.618 6.561 7.869 7.175 (0.614)
 Duration of paid work episodes 102.199 97.676 70.703 56.976 (

Table 2. Summary statistics of individual variables
 Commuters Teleworkers Diff.
 VARIABLES Mean S.D. Mean S.D. p-value
 A) FEMALES
 Paid work time 436.454 145.819 310.502 198.327 (

Table 3. Estimates on worker time allocations
 (1) (2) (3) (4) (5) (6)
 PAID WORK TIME UNPAID WORK TIME LEISURE TIME
 VARIABLES Women Men Women Men Women Men

 Being a teleworker -0.506*** -0.343*** 0.465*** 0.633*** 0.262*** 0.202*
 (0.054) (0.051) (0.105) (0.128) (0.079) (0.105)
 Age 0.008 0.000 0.060*** 0.095*** 0.015 0.017
 (0.007) (0.006) (0.020) (0.027) (0.014) (0.016)
 Age squared -0.001 -0.000 -0.004* -0.008*** -0.001 -0.001
 (0.001) (0.001) (0.002) (0.003) (0.002) (0.002)
 Secondary education 0.031 -0.049 0.339 0.146 -0.215** -0.057
 (0.056) (0.046) (0.225) (0.214) (0.105) (0.127)
 University education 0.101* -0.063 0.253 0.383* -0.355*** -0.097
 (0.058) (0.048) (0.230) (0.215) (0.115) (0.123)
 Immigrant 0.020 -0.029 0.020 -0.251 -0.136 0.046
 (0.041) (0.044) (0.125) (0.164) (0.102) (0.099)
 UK citizen 0.037 -0.039 -0.099 -0.013 0.004 0.223*
 (0.041) (0.044) (0.135) (0.189) (0.104) (0.134)
 Married and living with 0.018 -0.004 0.093 -0.330*** -0.035 -0.074
 (0.025) (0.024) (0.085) (0.110) (0.060) (0.080)
 Number of family unit members -0.005 0.005 -0.122*** -0.062 -0.066* -0.051
 (0.011) (0.012) (0.045) (0.049) (0.035) (0.040)
 Number of children aged 0-4 -0.005 0.010 -0.028 0.144 -0.139** -0.174**
 (0.030) (0.026) (0.090) (0.103) (0.068) (0.071)
 Number of children -0.023 0.001 0.188*** 0.115 -0.019 0.042
 (0.019) (0.021) (0.067) (0.080) (0.047) (0.055)
 Employee in the public sector -0.021 0.034 -0.045 0.006 0.009 -0.047
 (0.034) (0.038) (0.098) (0.150) (0.073) (0.110)
 Part time worker -0.172*** -0.086* 0.262*** 0.061 0.062 -0.098
 (0.037) (0.050) (0.101) (0.202) (0.074) (0.152)
 Net monthly earnings 0.004** -0.003 0.001 -0.004 -0.001 0.003
 (0.002) (0.002) (0.004) (0.007) (0.005) (0.003)
 Hours usually worked per week 0.007*** 0.007*** -0.006 -0.010* -0.009*** -0.015***
 (0.002) (0.002) (0.005) (0.006) (0.003) (0.005)

 Constant 5.513*** 6.118*** 3.219*** 0.677 5.485*** 4.715***
 (0.193) (0.160) (0.529) (0.784) (0.373) (0.578)

 Industry FE Yes Yes Yes Yes Yes Yes
 Observations 1,567 1,509 1,567 1,509 1,567 1,509
 R-squared 0.267 0.156 0.121 0.079 0.066 0.058
Note: The sample (UKTUS 2015) is restricted to employees who filled in their diaries on working days. Robust standard errors
in parentheses. The dependent variables are the log-of-minutes spent in paid work (Columns (1-2)), unpaid work (Columns (3-
4)), and leisure (Columns (5-6)). Teleworkers are defined as those workers devoting zero minutes to commuting to/from work. *
Significant at the 90%; ** significant at the 95%; *** significant at the 99%.

 20

Table 4. Estimates on experienced enjoyment
 (1) (2) (3) (4) (5) (6)
 PAID WORK UNPAID WORK LEISURE
 EPISODES EPISODES EPISODES
VARIABLES Women Men Women Men Women Men

Being a teleworker -0.458*** 0.099 -0.267*** 0.091 -0.097 0.504***
 (0.092) (0.074) (0.092) (0.115) (0.100) (0.089)
Episode duration -0.002*** -0.002*** -0.003 -0.006** -0.000 0.003***
 (0.000) (0.000) (0.002) (0.003) (0.001) (0.001)
With: Spouse -0.163 0.529*** 0.413*** 0.136 0.394*** 0.371***
 (0.166) (0.162) (0.080) (0.106) (0.077) (0.079)
With: Child 0.801*** -1.189*** 0.539*** 0.288* 0.634*** -0.123
 (0.273) (0.377) (0.116) (0.162) (0.137) (0.139)

Constant 7.574*** 3.299*** 4.325*** 2.731*** 4.513*** 3.733***
 (0.467) (0.486) (0.571) (0.795) (0.607) (0.656)

 Socio-demographics Yes Yes Yes Yes Yes Yes
 Family controls Yes Yes Yes Yes Yes Yes
 Labor controls Yes Yes Yes Yes Yes Yes
 Industry FE Yes Yes Yes Yes Yes Yes
 Starting time FE Yes Yes Yes Yes Yes Yes
 Observations 11,592 11,682 8,372 4,314 9,746 9,235
 R-squared 0.039 0.064 0.045 0.068 0.037 0.078
Note: The sample (UKTUS 2015) is restricted to episodes of paid work (Columns (1-2)), unpaid work (Columns (3-4)), and leisure
(Columns (5-6)) of employees who filled in their diaries on working days. Robust standard errors in parentheses. The dependent
variable is the enjoyment experience while doing paid work activities (Columns (1-2)), unpaid work activities (Columns (3-4)), and
leisure activities (Columns (5-6)). Teleworkers are defined as those workers devoting zero minutes to commuting to/from work. *
Significant at the 90%; ** significant at the 95%; *** significant at the 99%.

 21

Appendix A: Additional results

 Table A1. Definitions of teleworkers
 Frequency Percent
 Default definition of teleworkers:
 Individuals reporting zero commuting 415 13.49%

 Alternative definitions of teleworkers:
 Some paid work at home 440 14.30%
 More than 1 hour of paid work at home 342 11.12%
 All the paid work at home 184 5.98%

 N. of individuals 3,076
 Note: The sample (UKTUS 2015) is restricted to employees who filled in their diaries
 on working days.

 22

Table A2. Additional estimates on worker time allocations
 (1) (2) (3) (4) (5) (6)
 PAID WORK TIME UNPAID WORK TIME LEISURE TIME
 VARIABLES Women Men Women Men Women Men
 PANEL A
 Doing some telework -0.252*** -0.291*** 0.334*** 0.408*** 0.126 0.143
 (0.051) (0.054) (0.100) (0.143) (0.080) (0.107)

 Controls Yes Yes Yes Yes Yes Yes
 Industry FE Yes Yes Yes Yes Yes Yes
 Observations 1,567 1,509 1,567 1,509 1,567 1,509
 R-squared 0.188 0.131 0.117 0.069 0.062 0.056
 PANEL B
 Doing at least 1h of telework -0.355*** -0.356*** 0.378*** 0.519*** 0.241*** 0.084
 (0.061) (0.065) (0.112) (0.157) (0.079) (0.135)

 Controls Yes Yes Yes Yes Yes Yes
 Industry FE Yes Yes Yes Yes Yes Yes
 Observations 1,567 1,509 1,567 1,509 1,567 1,509
 R-squared 0.205 0.140 0.117 0.071 0.065 0.055
 PANEL C
 Full telework -0.788*** -0.713*** 0.726*** 1.103*** 0.522*** 0.718***
 (0.074) (0.090) (0.124) (0.160) (0.091) (0.087)

 Controls Yes Yes Yes Yes Yes Yes
 Industry FE Yes Yes Yes Yes Yes Yes
 Observations 1,567 1,509 1,567 1,509 1,567 1,509
 R-squared 0.297 0.212 0.124 0.082 0.073 0.072
Note: The sample (UKTUS 2015) is restricted to employees who filled in their diaries on working days. Robust standard errors
in parentheses. The dependent variables are the log-of-minutes spent doing paid work (Columns (1-2)), unpaid work (Columns
(3-4)), and leisure (Columns (5-6)). Teleworkers are defined as those workers who do some paid work at home (Panel A); those
workers who do at least 1 hour of paid work at home (Panel B); or those workers who do all their paid work at home (Panel C).
Additional coefficients are available upon request. * Significant at the 90%; ** significant at the 95%; *** significant at the 99%.

 23

Table A3. Rate of individuals doing paid work, unpaid work, and leisure
 PAID WORK UNPAID WORK LEISURE
 Females Males Females Males Females Males
 Band starting at Telew. Comm. Telew. Comm. Telew. Comm. Telew. Comm. Telew. Comm. Telew. Comm.

 4am 0.023 0.040 0.046 0.073 0.000 0.011 0.005 0.009 0.004 0.014 0.024 0.017
 5am 0.035 0.047 0.082 0.081 0.017 0.034 0.013 0.034 0.028 0.027 0.024 0.034
 6am 0.069 0.052 0.133 0.100 0.086 0.081 0.084 0.058 0.051 0.052 0.055 0.056
 7am 0.107 0.112 0.206 0.201 0.143 0.131 0.085 0.071 0.087 0.046 0.099 0.053
 8am 0.236 0.324 0.399 0.411 0.169 0.087 0.056 0.038 0.091 0.050 0.116 0.059
 9am 0.353 0.569 0.532 0.643 0.169 0.074 0.093 0.027 0.138 0.061 0.081 0.044
 10am 0.451 0.709 0.595 0.727 0.174 0.074 0.072 0.032 0.107 0.046 0.082 0.052
 11am 0.431 0.699 0.619 0.695 0.182 0.069 0.088 0.040 0.155 0.058 0.114 0.080
 12am 0.366 0.525 0.454 0.521 0.212 0.071 0.144 0.048 0.101 0.084 0.094 0.088
 1pm 0.360 0.507 0.447 0.535 0.193 0.082 0.098 0.048 0.099 0.096 0.137 0.097
 2pm 0.407 0.577 0.555 0.655 0.217 0.095 0.095 0.052 0.132 0.103 0.128 0.081
 3pm 0.323 0.530 0.557 0.625 0.208 0.110 0.090 0.051 0.171 0.101 0.150 0.105
 4pm 0.308 0.388 0.462 0.491 0.177 0.165 0.123 0.078 0.153 0.135 0.204 0.136
 5pm 0.219 0.201 0.333 0.275 0.293 0.224 0.148 0.119 0.165 0.157 0.220 0.167
 6pm 0.150 0.111 0.190 0.135 0.257 0.247 0.186 0.131 0.223 0.216 0.235 0.263
 7pm 0.111 0.080 0.128 0.098 0.190 0.201 0.135 0.131 0.350 0.303 0.416 0.360
 8pm 0.113 0.064 0.114 0.082 0.123 0.173 0.080 0.112 0.432 0.414 0.489 0.471
 9pm 0.064 0.057 0.105 0.081 0.095 0.111 0.071 0.065 0.538 0.452 0.503 0.492
 10pm 0.048 0.045 0.076 0.075 0.038 0.068 0.057 0.053 0.406 0.320 0.371 0.350
 11pm 0.041 0.040 0.065 0.065 0.040 0.040 0.028 0.031 0.159 0.178 0.214 0.218
 12pm 0.037 0.040 0.051 0.071 0.018 0.012 0.009 0.020 0.078 0.080 0.107 0.115
 1am 0.045 0.040 0.051 0.069 0.000 0.008 0.009 0.019 0.000 0.031 0.032 0.048
 2am 0.037 0.035 0.055 0.068 0.003 0.004 0.014 0.007 0.000 0.012 0.007 0.016
 3am 0.019 0.032 0.047 0.063 0.000 0.003 0.010 0.004 0.010 0.011 0.014 0.008
Note: The sample (UKTUS 2015) is restricted to diaries of employees who filled in their diaries on working days.

 24

Table A4. Additional estimates on experienced enjoyment
 (1) (2) (3) (4) (5) (6)
 PAID WORK UNPAID WORK LEISURE
 EPISODES EPISODES EPISODES
 VARIABLES Women Men Women Men Women Men
 PANEL A
 Doing some telework -0.047 0.236*** -0.171* 0.195* -0.089 0.642***
 (0.097) (0.084) (0.090) (0.116) (0.100) (0.083)

 Controls Yes Yes Yes Yes Yes Yes
 Industry FE Yes Yes Yes Yes Yes Yes
 Observations 11,592 11,682 8,372 4,314 9,746 9,235
 R-squared 0.039 0.068 0.045 0.071 0.037 0.081
 PANEL B
 Doing at least 1h of telework -0.043 0.147 -0.129 0.249* 0.239** 0.716***
 (0.104) (0.115) (0.091) (0.129) (0.097) (0.093)

 Controls Yes Yes Yes Yes Yes Yes
 Industry FE Yes Yes Yes Yes Yes Yes
 Observations 11,592 11,682 8,372 4,314 9,746 9,235
 R-squared 0.039 0.067 0.044 0.072 0.038 0.080
 PANEL C
 Full telework 0.315 0.201 -0.069 0.057 0.345*** 0.565***
 (0.205) (0.173) (0.113) (0.159) (0.117) (0.113)

 Controls Yes Yes Yes Yes Yes Yes
 Industry FE Yes Yes Yes Yes Yes Yes
 Observations 11,592 11,682 8,372 4,314 9,746 9,235
 R-squared 0.039 0.067 0.044 0.071 0.038 0.078
Note: The sample (UKTUS 2015) is restricted to episodes of paid work (Columns (1-2)), unpaid work (Columns (3-4)), or leisure
(Columns (5-6)) of employees who filled in their diaries on working days. Robust standard errors in parentheses. The dependent
variable is the enjoyment experience while doing paid work activities (Columns (1-2)), unpaid work activities (Columns (3-4)), and
leisure activities (Columns (5-6)). Teleworkers are defined as those workers who do some paid work at home (Panel A); those
workers who do at least 1 hour of paid work at home (Panel B); or those workers who do all their paid work at home (Panel C).
* Significant at the 90%; ** significant at the 95%; *** significant at the 99%.

 25

Table A5. Additional summary statistics of individual variables
 FEMALES MALES
 Commuters Teleworkers Diff. Commuters Teleworkers Diff.
 VARIABLES Mean S.D. Mean S.D. p-value p-value S.D. Mean S.D. p-value

 Age 39.616 13.264 42.050 11.115 (0.021) 39.062 12.888 40.815 12.301 (0.046)
 Basic education 0.049 0.216 0.027 0.164 (0.152) 0.052 0.222 0.033 0.180 (0.157)
 Secondary education 0.476 0.500 0.371 0.484 (0.008) 0.476 0.500 0.415 0.494 (0.043)
 University education 0.475 0.500 0.602 0.491 (